Processing method and processing device based on multiple sequence alignment genetic algorithm

Abstract

The invention provides a processing method and a processing device based on a multiple sequence alignment genetic algorithm. The processing method includes: building a brief classification identification model, determining a parameter set for special practical problems, performing code design on the determined parameter set, briefly classifying an initialized sub population, performing arithmetic calculation for the sub population and judging performance of the sub population, and processing the sub population according to judging results. The scheme is favorable for efficient comparison identification, overcomes shortcomings that the genetic algorithm is slow in convergence and apt to fall into local optimum and shortcomings that the existing attribute reduction algorithm is high in calculation complexity, not suitable for scale data reduction, not enough in describing of attribute collection and the like, and improves processing efficiency.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a processing method and device based on a multiple sequence alignment genetic algorithm. Background technique [0002] At present, sequence alignment (Sequence Alignment, SA) is the most basic and most important key technology for analyzing and exploring the essential characteristics of biological information. Through sequence alignment, important information such as function, structure and evolution in biological sequences can be found. Multiple Sequence Alignment (Multiple Sequence Alignment, MSA) is a basic and effective method for aligning multiple sequences in biological information. , pharmaceutical medical treatment and information processing and other aspects also have good application and important reference. [0003] The comparison of biological sequences is the most basic and important processing work in bioinformatics. Through sequence comparison, the ...

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Problems solved by technology

[0005] At present, although heuristic algorithms for sequence alignment and parallel computing new technologies, new methods, and new applications are emerging one after another at home and abroad, such as the genetic algorithm for multiple sequence alignment, the ant colony algorithm Ant-Align, cluster analysis, particle swarm Algorithm, simulated annealing algorithm, A-star algorithm, Fourier transform method and other neural network methods and single algorithms, but there are certain defects or deficiencies in comparison accuracy, speed, stability, etc., especially in the specific key Algorithms need to be further improved, innovated and perfected

, which effectively solves the problem of local convergence and strengthens the ability of the algorithm to seek the global optimal solution. However, there are certain defects or deficiencies in comparison accuracy, speed, stability, etc.

At present, although the single rough set (Roug Set, RS) used to deal with fuzzy and uncertain information technology, or the LVQ neural network classification and recognition algorithm has improved in some aspects such as local optimal solution convergence, but in many There are still shortcomings in the efficiency and speed of sequence alignment

[0006] In view of the shortcomings of the existing genetic algorithm, such as slow convergence and easy to fall into local optimum, and the current attribute reduction algorithm has high computational complexity, is not suitable for large-scale data reduction, and is insufficient in describing attribute sets, etc., Proposing a processing method and device based on multiple sequence alignment genetic algorithm is one of the urgent problems to be solved in the field of information processing technology

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